Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Countries citing papers authored by David Heckerman
Since
Specialization
Citations
This map shows the geographic impact of David Heckerman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Heckerman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Heckerman more than expected).
This network shows the impact of papers produced by David Heckerman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Heckerman. The network helps show where David Heckerman may publish in the future.
Co-authorship network of co-authors of David Heckerman
This figure shows the co-authorship network connecting the top 25 collaborators of David Heckerman.
A scholar is included among the top collaborators of David Heckerman based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with David Heckerman. David Heckerman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Listgarten, Jennifer & David Heckerman. (2007). Determining the number of non-spurious arcs in a learned DAG model: investigation of a Bayesian and a frequentist approach. Uncertainty in Artificial Intelligence. 251–258.14 indexed citations
Thiesson, Bo, David Maxwell Chickering, David Heckerman, & Christopher Meek. (2004). ARMA time-series modeling with graphical models. arXiv (Cornell University). 552–560.8 indexed citations
13.
Hulten, Geoff, David Maxwell Chickering, & David Heckerman. (2003). Learning Bayesian Networks From Dependency Networks: A Preliminary Study. International Conference on Artificial Intelligence and Statistics. 141–148.14 indexed citations
14.
Thiesson, Bo, et al.. (1997). Learning Mixtures of Bayesian Networks.18 indexed citations
15.
Suermondt, Henri J., Gregory F. Cooper, & David Heckerman. (1990). A combination of cutset conditioning with clique-tree propagation in the Pathfinder system. Uncertainty in Artificial Intelligence. 245–254.14 indexed citations
16.
Heckerman, David, et al.. (1989). The Pathfinder System. PubMed Central. 203–207.1 indexed citations
17.
Horvitz, Eric, et al.. (1989). Heuristic Abstraction in the Decision-Theoretic Pathfinder System. PubMed Central. 178–182.8 indexed citations
18.
Cooper, Gregory F., Eric Horvitz, & David Heckerman. (1988). A Method for Temporal Probabilistic Reasoning. 80(3). 40–1.7 indexed citations
Horvitz, Eric, David Heckerman, & Curtis P. Langlotz. (1986). A framework for comparing alternative formalisms for plausible reasoning. National Conference on Artificial Intelligence. 210–214.53 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.